Long wave infrared (LWIR) hyperspectral imaging data can be used for gas plume identification. This technology, although incredibly useful for identifying gas plumes without taking direct measurements from the source area, has a few problems associated with the efficient use of the technology. One of these problems is false gas identifications that may occur when the hyperspectral data is analyzed to identify chemical gas plumes.
Generally, a library of hyperspectral gas data signatures is used to compare against an observed hyperspectral gas data signature based on an analysis logic. If the observed hyperspectral gas data signature compares favorably to at least one of the library of hyperspectral gas data signatures, then the logic indicates that this particular gas is present near the source area where the hyperspectral data was collected. However, many items may cause a hyperspectral data signature which may be similar enough to a gas' hyperspectral data signature to appear to be that gas, when in fact it is not that gas. When these items are falsely identified as a gas, they are typically called ‘false alarms’ or ‘false hits.’ When a gas is properly identified by analysis of the hyperspectral data, it may trigger an alarm. Generally, each alarm is reviewed by someone to determine if it is a ‘false alarm’ or is actually a gas present in the source area. There is a tremendous amount of manual workload that is performed in order to pick through the results of thousands of hyperspectral data sets and to eliminate likely false alarms from the data.
Therefore, it would be very beneficial to have an automated method of eliminating or greatly reducing the amount of false alarms that are indicated through hyperspectral data analysis to increase the efficiency and ease of use of hyperspectral data analysis to identify gas plumes.